Use a common NormMlpClassifierHead across MaxViT, ConvNeXt, DaViT Add EfficientFormer-V2 model, update EfficientFormer, and refactor LeViT (closely related architectures). Weights on HF hub. New EfficientFormer-V2 arch, significant refactor from original at (https://github.com/snap-research/Effic...
c2, 1, s, autopad(1, p, d), groups=g, dilation=d, bias=False) # add 1x1 conv self.bn = nn.BatchNorm2d(c2) self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() def forward(self, x): """Apply convolution, ...
(type='Deconv') up_norm_cfg = norm_cfg up_act_cfg = dict(type='ReLU') up_in_channels = inner_channels * 2 up_bias = use_bias middle = [submodule] upper = [] if is_outermost: down_act_cfg = None down_norm_cfg = None up_bias = True up_norm_cfg = None upp...
MOBILENET.WEIGHT_DECAY) if cfg.MOBILENET.REGU_DEPTH: depthwise_regularizer = regularizer else: depthwise_regularizer = None with slim.arg_scope([slim.conv2d, slim.separable_conv2d], trainable=is_training, weights_initializer=weights_init, activation_fn=tf.nn.relu6, normalizer_fn=slim.batch_norm,...
defpreprocess_batch(self,batch):batch['img']=batch['img'].to(self.device,non_blocking=True).float()/255returnbatchdefset_model_attributes(self):self.model.nc=self.data['nc']self.model.names=self.data['names']self.model.args=self.argsdefget_model(self,cfg=None,weights=None,verbose=True...
(64,128,3,norm_fn=norm_fn,stride=2,padding=(0,1,1),indice_key='spconv4',conv_type='spconv'),SparseBasicBlock(128,128,norm_fn=norm_fn,indice_key='res4'),SparseBasicBlock(128,128,norm_fn=norm_fn,indice_key='res4'),)last_pad=0last_pad=self.model_cfg.get('last_pad',last_...
(m.conv, m.bn) # update conv# 融合后conv的参数就包含了bn的用途, 所以可以删除bn层delattr(m, 'bn') # remove batchnorm# 由于不需要bn层, 所以forward函数需要改写:# self.act(self.bn(self.conv(x))) -> self.act(self.conv(x))m.forward = m.forward_fuse # update forwardself.info()...
# 需要导入模块: from tensorflow.contrib import slim [as 别名]# 或者: from tensorflow.contrib.slim importseparable_conv2d[as 别名]def_context(self, net, is_training, name, iter):num_layers = cfg.MEM.CT_L xavier = tf.contrib.layers.variance_scaling_initializer()assertnum_layers %2==1conv...
LayerNorm ConvNeXt 组网 PASSL 已支持 ConvNeXt 更详细内容可见 https://github.com/PaddlePaddle/PASSL/tree/main/configs/convnext ConvNeXt 性能图如下所示 总结一下 ConvNeXt 是一个非常非常工程化的东西,确实很简单思路很soild,确实没有什么novelty,这也进一步证明了视觉Transformer 威力其实并不一定来自attention...
(self,dim,num_heads,mlp_ratio=4.,qkv_bias=False,qk_scale=None,drop=0.,attn_drop=0.,drop_path=0.,act_layer=nn.GELU,norm_layer=nn.LayerNorm):super().__init__()self.norm1=nn.LayerNorm(dim)self.conv1=nn.Conv2d(dim,dim,1)self.conv2=nn.Conv2d(dim,dim,1)self.attn=nn.Conv2d...